1. Field of the Invention
The present invention relates generally to point cloud data, and in particular, to a method, apparatus, and article of manufacture for extracting the level and reference grid of floor plan information of a building from point cloud data.
2. Description of the Related Art
(Note: This application references a number of different publications as indicated throughout the specification by references enclosed in brackets, e.g. [x]. Such references may indicate the first named author and year of publication e.g., [Okorn et al. 2010]. A list of these different publications ordered according to these references can be found below in the section entitled “References.” Each of these publications is incorporated by reference herein.)
Building information models (BIM) are being increasingly used throughout a building's lifecycle in the architecture, engineering, and construction (AEC) industry. BIMs can be used for many purposes, from planning and visualization in the design phase, to inspection during the construction phase, and to energy efficiency analysis and security planning during the facility management phase. However, BIMs are often not available for most existing buildings. Further, the BIM created during the design phase may vary significantly from what was actually built. As a result, there is strong interest in creating BIMs of the actual as-built building.
Laser scanners are rapidly gaining acceptance as a tool for three-dimensional (3D) modeling and analysis in the architecture, engineering, and construction (AEC) domain. With technological development/evolution, laser scanners are capable of acquiring range measurements at rates of tens to hundreds of thousands of points per second, at distances of up to a few hundred meters, and with a measurement error on the scale of millimeters. These characteristics make them well suited for densely capturing the as-built information of building interiors and exteriors. Typically, laser scanners are placed in various locations throughout and around a building. The scans from each location are registered and aligned to form a point cloud in a common coordinate system. Multiple scans are often needed to capture the point cloud of a whole building.
Currently, as-built BIMs are mostly created interactively from the point cloud data generated by the laser scanners. However, this creation process is labor-intensive and error-prone. Thus, there is a lack of research work and commercial software tools currently available for automatically extracting building datum information from point clouds.
In most applications and software tools, floor plan modeling is achieved by first creating a horizontal slice of the environment [Li et al. 2011] and then using various two-dimensional (2D) geometric modeling methods [Nguyen et al. 2005], including RANSAC (RANdom SAmple Consensus), iterative end point fitting, and the Hough transform, to extract the linear geometry in the horizontal slice. For example, Okorn et al. [Okorn et al. 2010] examines floor plan modeling of wall structures to create blueprints from terrestrial laser scanning points. The direction of gravity (the vertical direction) is assumed to be known. A two-dimensional histogram is created from the points projected onto the ground plane. Linear structures from this histogram are then extracted using a Hough transform.
However, a floor plan modeling method based on only one single horizontal slice of the environment does not take the whole building interior environment information into consideration, which means some elements might be missing from the single slice. Thus, a single slice does not adequately represent the whole floor plan structure. Furthermore, it is difficult to determine the slice height most appropriate for generating the floor plan and therefore it is difficult to automatically select the height of the slice. Moreover, a single slice method does not filter out points obtained from the objects and clutter existing in the interior environment of the building, which further prevents the generation of a clear and accurate floor plan map. For point cloud data captured by terrestrial laser scanners, wall points near the floor surface are more likely to be obstructed by furniture and other clutter. Additionally, the wall points near the ceiling surface are likely to be obstructed by other MEP (mechanical, electrical, and plumbing) utilities or decorations.
On the other hand, a three-dimensional (3D) method first models the planar wall, floor, and ceiling surface and then creates the levels and floor plan information with a cross-section step. However, such a method is computation-heavy and due to the existence of noise and outliers, the wall, floor, and ceiling surface cannot be modeled perfectly.
Other related works on floor plan modeling/mapping have mainly focused on robotics research [Schröter et al. 2002]. Such floor plan maps are usually generated by robots equipped with laser scanners. The main purpose of the maps is for use in robotic navigation. Therefore, the research on generating these types of navigation maps do not place much emphasis on being highly accurate or complete.
In view of the above, it is desirable to extract/determine the level and floor plan information of a building from building point cloud data in an easy and efficient manner. Software tools are needed for processing point clouds to improve the ability to handle the enormous point clouds produced by laser scanners and to integrate the use of point cloud data into BIM modeling software.